Discovery and Fusion of Uncertain Knowledge in Data

この資料の関連情報

With the rapid development of data acquisition, IT infrastructures,social networks and Web2.0 applications, more and more massive, heterogeneous, uncertain,dynamically changing and socialized data are generated and stored in distributedsystems. Discovering implied knowledge from data is always the topic with greatattention for data understanding, data utilization and information services,where uncertainty is ubiquitous. We adopt Bayesian network (BN), one of thepopular and important probabilistic graphical models, as the effectiveframework for representing and inferring uncertain knowledge by means ofqualitative and quantitative manners. In this report, we present our studies onacquisition, representation, inference and fusion of uncertain knowledgeimplied in data, oriented to the applications of provenance analysis and userpreference modeling.